Wind energy offers the potential to reduce carbon emissions while increasing energy independence and bolstering economic development. However, wind energy has a larger land footprint per Gigawatt (GW) than most other forms of energy production and has known and predicted adverse effects on wildlife. The Northern Great Plains (NGP) is home both to some of the world’s best wind resources and to remaining temperate grasslands, the most converted and least protected ecological system on the planet. Thus, appropriate siting and mitigation of wind development is particularly important in this region. Steering energy development to disturbed lands with low wildlife value rather than placing new developments within large and intact habitats would reduce impacts to wildlife. Goals for wind energy development in the NGP are roughly 30 GW of nameplate capacity by 2030. Our analyses demonstrate that there are large areas where wind development would likely have few additional impacts on wildlife. We estimate there are ∼1,056 GW of potential wind energy available across the NGP on areas likely to have low-impact for biodiversity, over 35 times development goals. New policies and approaches will be required to guide wind energy development to low-impact areas.

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We studied the habitat selection of pronghorn (Antilocapra americana) during seasonal migration; an important period in an animal’s annual cycle associated with broad-scale movements. We further decompose our understanding of migration habitat itself as the product of both broad- and fine-scale behavioral decisions and take a multi-scale approach to assess pronghorn spring and fall migration across the transboundary Northern Sagebrush Steppe region. We used a hierarchical habitat selection framework to assess a suite of natural and anthropogenic features that have been shown to influence selection patterns of pronghorn at both broad (migratory neighborhood) and fine (migratory pathway) scales. We then combined single-scale predictions into a scale-integrated step selection function (ISSF) map to assess its effectiveness in predicting migration route habitat. During spring, pronghorn selected for native grasslands, areas of high forage productivity (NDVI), and avoided human activity (i.e., roads and oil and natural gas wells). During fall, pronghorn selected for native grasslands, larger streams and rivers, and avoided roads. We detected avoidance of paved roads, unpaved roads, and wells at broad spatial scales, but no response to these features at fine scales. In other words, migratory pronghorn responded more strongly to anthropogenic features when selecting a broad neighborhood through which to migrate than when selecting individual steps along their migratory pathway. Our results demonstrate that scales of migratory route selection are hierarchically nested within each other from broader (second-order) to finer scales (third-order). In addition, we found other variables during particular migratory periods (i.e., native grasslands in spring) were selected for across scales indicating their importance for pronghorn. The mapping of ungulate migration habitat is a topic of high conservation relevance. In some applications, corridors are mapped according to telemetry location data from a sample of animals, with the assumption that the sample adequately represents habitat for the entire population. Our use of multi-scale modelling to predict resource selection during migration shows promise and may offer another relevant alternative for use in future conservation planning and land management decisions where telemetry-based sampling is unavailable or incomplete.

The number of certified genetic counselors (CGCs) in the genetic counseling workforce has increased over the past few decades as the number of training programs increases and CGCs expand into new patient‐facing and non‐patient‐facing roles. Few studies have explored the distribution of CGCs across the United States. We sought to identify the U.S. geographical regions with the highest number of CGCs and those regions where the physical presence of CGCs is sparser. Deidentified city, state, and ZIP code information for each CGC in the United States were obtained from the American Board of Genetic Counseling (ABGC) database. A countrywide analysis of the distribution of CGCs was completed using geographic information system (GIS) mapping software. The data were organized into U.S. metropolitan or micropolitan statistical areas, if applicable, and analyzed by CGC per capita. We included a total of 4,554 data points (92.2%) in the analysis. Results showed there is one CGC for every 71,842 people nationwide. Of 3,141 total counties (or county equivalents) in the United States, 535 counties had at least one CGC (17.0%). The majority (98.7%) of CGCs live or work within metropolitan statistical areas (MSAs), which are defined by this study as geographical areas with greater than 50,000 people. Of the MSAs with a CGC, approximately half have more than one CGC per 100,000 people. These results are consistent with the overall distribution of the U.S. population. We believe that the MSAs with the most CGCs per capita are due to associations with specific institutions, that is, genetic counseling training programs, health system headquarters, or genetic laboratories. Although the present study cannot draw definite conclusions regarding direct patient care services provided by CGCs, it does provide a snapshot of current CGC distribution within the country. Knowing the distribution of CGCs provides a tool to conduct further workforce analyses to determine the number of CGCs needed to serve the U.S. population.